sqllite

This MCP server provides a Python-based interface for interacting with SQLite databases. It lets you ask natural-language questions, automatically translate them into SQL, create and manage databases and tables, run queries, analyze results, and visualize data through an integrated workflow. You can chain SQL-backed analysis with visualization via the Visualization MCP server to produce charts and dashboards directly from your data. Use it to perform tasks such as creating new databases, defining schemas, inserting or updating data, and querying for insights in an intuitive, AI-assisted manner.

Prerequisites:

• Python 3.8+ installed on your system
• Optional: a virtual environment tool (venv is fine)

Install and run:

1. Create and activate a virtual environment (recommended): python3 -m venv .venv --upgrade-deps source .venv/bin/activate

2. Install the MCP CLI and dependencies for SQLite workflow: pip install "mcp[cli]" httpx

3. (Optional) Install Faker for mock data generation during testing: pip install faker

4. Ensure your SQLite workflows are available via main.py in the repository root (as indicated by this MCP server).

5. Run the MCP server (example): python main.py

If you’re integrating with Claude or another MCP client, configure the appropriate client to point to this server using the provided command and arguments.

Tips and caveats:

• Ensure Python virtual environment is activated before running the server to isolate dependencies.
• When using Claude Desktop or another client, you’ll typically point the client at the main.py entry via the configured path (as shown in the README example).
• If you plan to visualize results, pair this SQLite MCP server with the Visualization MCP server and send query results to it for charting.
• Common issues: missing dependencies, incorrect Python path, or mismatched SQLite database paths. Verify you’re running the exact main.py referenced by the MCP configuration and that the Python environment includes sqlite3 (or the standard library module for SQLite).
• You can extend sqlite_mcp_server.py to add custom SQL operations or integrate additional data sources as needed.